QumulusAI Signs $32 Million, Two-Year NVIDIA Blackwell B300 Agreement With AI Inference Platform Provider
The customer, an inference platform serving production generative AI applications, will run image, video and other generative workloads on dedicated Blackwell B300 clusters from QumulusAI’s hyperdistributed AI cloud
QumulusAI , a neocloud infrastructure provider purpose-built for the AI computing era, announced a signed two-year agreement, valued at more than $32 million, to supply NVIDIA Blackwell B300 capacity to an AI inference platform provider focused on generative AI applications. The agreement includes renewal options, with capacity expected to come online in the fall of 2026.
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QumulusAI Signs $32 Million, Two-Year NVIDIA Blackwell B300 Agreement With AI Inference Platform Provider
The customer’s platform delivers fast, scalable infrastructure for generative AI applications, serving developers and enterprises that run image, video and other generative models where speed and reliability determine the user experience. Under the agreement, QumulusAI will provide dedicated GPU clusters that give the platform committed, high-performance capacity as demand from its customers scales.
Serving generative media workloads at scale typically calls for sustained, dedicated compute rather than opportunistic spot capacity — a dynamic reflected in multiyear commitments such as this agreement. Capacity will be served from QumulusAI’s U.S. data center footprint. The company’s demand-led deployment model places capacity into available pockets of power across a distributed network of colocation and owned facilities, enabling it to bring GPU capacity online in months, not years.
The agreement adds a two-year commitment of more than $32 million to QumulusAI’s book of business.
“Generative media workloads put real pressure on inference infrastructure — images and video are compute-intensive to serve, and the user experience depends on speed,” said Mike Maniscalco, CEO of QumulusAI. “This agreement reflects a pattern we’re seeing in our own business: inference customers want dedicated, committed capacity they can count on, and our model is built to put that capacity to work quickly.”
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